The Big Five default brain: functional evidence

ORIGINAL ARTICLE

The Big Five default brain: functional evidence

Adriana Sampaio • Jose Miguel Soares •

Joana Coutinho • Nuno Sousa • Oscar F. Goncalves

Received: 6 December 2012 / Accepted: 5 July 2013 / Published online: 24 July 2013

� Springer-Verlag Berlin Heidelberg 2013

Abstract Recent neuroimaging studies have provided

evidence that different dimensions of human personality

may be associated with specific structural neuroanatomic

correlates. Identifying brain correlates of a situation-inde-

pendent personality structure would require evidence of a

stable default mode of brain functioning. In this study, we

investigated the correlates of the Big Five personality

dimensions (Extraversion, Neuroticism, Openness/Intel-

lect, Agreeableness, and Conscientiousness) and the default

mode network (DMN). Forty-nine healthy adults com-

pleted the NEO-Five Factor. The results showed that the

Extraversion (E) and Agreeableness (A) were positively

correlated with activity in the midline core of the DMN,

whereas Neuroticism (N), Openness (O), and Conscien-

tiousness (C) were correlated with the parietal cortex

system. Activity of the anterior cingulate cortex was pos-

itively associated with A and negatively with C. Regions of

the parietal lobe were differentially associated with each

personality dimension. The present study not only confirms

previous functional correlates regarding the Big Five per-

sonality dimensions, but it also expands our knowledge

showing the association between different personality

dimensions and specific patterns of brain activation at rest.

Keywords Personality � Imaging � fMRI � Default

mode network � Brain � Rest

Introduction

The question of whether the behavior is situation- or per-

sonality-specific is probably one of the longest standing

debates in psychology (Mischel 1968). Several resolutions of

the debate have been advanced due to the need for a refined

conceptualization of personality dimensions (Fleeson and

Noftle 2009), namely by building on the contributions of

what is coming to be known as personality neuroscience

(DeYoung 2010). In fact, personality neuroscience methods

can be instrumental in identifying neurobiological correlates

of different personality dimensions. What is required is a

reliable model of different personality dimensions, such as

The Big Five model developed by Costa and McRae (1992),

as well as a methodology capable of identifying consistent

biological markers underlying different personality dimen-

sions that are situation-independent.

Therefore, the understanding of the neuroanatomical

correlates of each of the Big Five components can be a

valuable tool to identify more stable/biological markers of

personality. Previous studies have been attempting to map

the brain regions associated with Extraversion and

A. Sampaio and J. M. Soares share equal first authorship.

Electronic supplementary material The online version of thisarticle (doi:10.1007/s00429-013-0610-y) contains supplementarymaterial, which is available to authorized users.

A. Sampaio (&) � J. Coutinho � O. F. Goncalves

Neuropsychophysiology Lab, CIPsi, School of Psychology,

University of Minho, 4710-057 Braga, Portugal

e-mail: [email protected]

J. M. Soares � N. Sousa

Life and Health Sciences Research Institute,

University of Minho, Braga, Portugal

J. M. Soares � N. Sousa

ICVS-3Bs PT Government Associate Laboratory,

Braga/Guimaraes, Portugal

O. F. Goncalves

Department of Counseling and Applied Educational Psychology,

Bouve College of Health Sciences, Northeastern University,

Boston, USA

123

Brain Struct Funct (2014) 219:1913–1922

DOI 10.1007/s00429-013-0610-y

Neuroticism; higher Extraversion scores were associated

with thinner cortical gray matter in regions of the right

inferior prefrontal cortex (PFC) and the fusiform gyrus,

whereas higher Neuroticism scores were associated with a

thinner cortex in the anterior regions of the left orbitofrontal

cortex (Wright et al. 2006). More recently, in a study with

116 healthy volunteers, DeYoung et al. (2010) presented the

first report on the association between all the Big Five

personality components and brain volumetry. Overall, the

authors were able to find significant neuroanatomical cor-

relates for most of the five personality dimensions. More

specifically, Extraversion was associated with increased

medial orbitofrontal cortex volume; Neuroticism with

reduced volume in the dorsomedial PFC and part of the left

medial temporal lobe, as well as with increased volume in

the mid-cingulate gyrus; Agreeableness with an increased

posterior cingulate cortex (PCC) and a decreased posterior

left superior temporal sulcus volumes; Conscientiousness

with increased volume in the middle left frontal gyrus; and,

finally, no significant associations were found for the

Openness/Intellect trait. As pointed out by the authors, this

study offers initial support for the neuroanatomical

dimensions of the Big Five personality trait taxonomy.

Building on these previous studies, it would be important to

see if these structural neuroanatomical findings would be

confirmed at the functional level. Indeed, functional studies

have been providing evidence regarding an association

between Extraversion and greater cerebral flow at rest (rCBF),

in regions such as the anterior cingulate gyrus, right insular

cortex, bilateral temporal lobes, pulvinar nucleus of the thal-

amus, posterior parietal lobe, and left amygdala (Johnson et al.

1999). Extraversion has also been correlated with increased

glucose metabolism at rest in the orbitofrontal cortex (Dec-

kersbach et al. 2006), and right putamen (Kim et al. 2008).

Neuroticism has been associated with decreased resting

regional cerebral glucose metabolism in the medial PFC (Kim

et al. 2008), and insular cortex (Deckersbach et al. 2006).

Finally, higher Openness/Intellect scores were correlated with

resting state rCBF in the dorsolateral PFC, the anterior cin-

gulate gyrus, and the orbitofrontal cortex (Sutin et al. 2009).

Overall, these studies provide initial support for the

existence of associations between brain activity in several

brain regions during rest (particularly the medial PFC) and

Neuroticism and Extraversion. Nevertheless, this initial

evidence, a full understanding of the relationship between

an integrated overview of the Big Five personality dimen-

sions and specific patterns of brain activation is still lacking.

As suggested by DeYoung et al. (2010), future studies

should provide an account of data that may address all brain

systems simultaneously. Additionally, identifying the brain

correlates of a stable personality structure would require

evidence of a stable default mode of brain functioning

associated with the different personality dimensions.

To explore brain functioning that is characteristic of

different personality dimensions in a task-independent

context, we decided to analyze the correlates of the Big Five

and the default mode network (DMN) (Raichle et al. 2001),

taking into account the specificity of this resting state net-

work (RSN) in internal processing and previous evidence

suggesting that individual differences in DMN activity

could be able to elucidate differences in personality (Wei

et al. 2012). The DMN is a network of brain cortical areas

that present high metabolic activity when the brain is ‘‘at

rest’’ and the individual is not focused on any external

demand. This RSN corresponds to a high degree of func-

tional connectivity between various interacting brain areas.

Typically, the DMN comprises areas of the PCC and

adjacent precuneus (PCu); the medial prefrontal cortex

(MPFC); medial, lateral and inferior parietal cortex (Rai-

chle et al. 2001), and medial temporal cortex (Buckner et al.

2008). The DMN has been associated with multiple and

dissociated components thought to serve important cogni-

tive and emotional functions (Andrews-Hanna et al. 2010),

such as supporting internal mental activity detached from

the external world (Mason et al. 2007), autobiographical

memory (Raichle and Snyder 2007), integrating cognitive

and emotional processing (Greicius et al. 2003), and con-

necting internal and external attention in monitoring the

world around us. Therefore, taking into account evidence

showing that the DMN, a stable network of the brain,

comprising multiple and dissociated components

(Andrews-Hanna et al. 2010), we hypothesized that these

may also be differentially related to stable personality traits.

While there is substantial evidence for an association

between DMN abnormalities and psychiatric disorders [cf.

(Cherkassky et al. 2006; Kennedy and Courchesne 2008;

Zhou et al. 2007)], studies exploring the functional signif-

icance of DMN patterns for stable personality dimensions

are still scarce. However, indirect evidence supports the

idea that functional connectivity patterns may underlie the

Big Five model personality organization (Wei et al. 2011;

Volkow et al. 2011). For example, Volkow et al. (2011)

studied the role of different brain regions’ glucose meta-

bolic activity at rest in positive emotionality (a personality

trait associated with well-being, achievement/motivation

and social closeness). The authors showed that this per-

sonality dimension was positively correlated with metabo-

lism in the orbitofrontal, anterior cingulate, middle and

lateral frontal, precuneus, parietal, superior and middle

temporal and fusiform cortices, while negative emotionality

and constraint were not correlated with any of these brain

regions. As pointed out by the authors, some of these

regions (the precuneus, superior parietal lobe) are compo-

nents of the DMN, which indicates that increased activity of

the DMN at rest may represent a neurobiological marker for

a positive emotionality trait (Volkow et al. 2011). In

1914 Brain Struct Funct (2014) 219:1913–1922

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addition, there is also evidence that personality traits predict

resting state functional connectivity (RSFC). Adelstein

et al. (2011) using a priori seeds, showed that Neuroticism

and Extraversion predicted connectivity between seed

regions (precuneus and ACC) and dorsomedial PFC and

lateral paralimibic regions, respectively. Openness to

Experience predicted RSFC with the midline ‘hubs’ of the

DMN and dorsolateral prefrontal activity; Agreeableness

predicted RSFC with posteromedial extrastriate regions and

Conscientiousness predicted RSFC with the medial tem-

poral lobe. Building on previous research demonstrating

that several brain regions are associated with specific per-

sonality dimensions and that a brain default network may

underlie the stability of distinct, stable psychological con-

ditions; the objective of the current study is to explore the

relationship between the personality dimensions described

by the Big Five model and the overall DMN’s activity

patterns. Therefore, we expected that the midline core of the

DMN (posterior cingulate, PCu, and MPFC) would be dif-

ferently associated with personality traits (Andrews-Hanna

et al. 2010) related with self-referential processing (e.g.,

autobiographical conditions, self-relevant and affective

decisions) as Neuroticism, Agreeableness, and Extraversion

(Deckersbach et al. 2006; Kim et al. 2008; Adelstein et al.

2011; Johnson et al. 1999; Cavanna and Trimble 2006). In

contrast, the parietal lobe and medial temporal cortex sys-

tems would be related with Openness to Experience/Intel-

lect and Conscientiousness, personality traits more related

with non-self processing (e.g., integration of cognitive and

emotional processing, connecting internal and external

attention, performing episodic judgments about the future)

(DeYoung et al. 2010; Behrmann et al. 2004).

Methods and materials

Participants

Forty-nine healthy volunteers participated in this study (30

female and 19 male) with a mean age of 25.0 (SD = 5.3)

years, ranging from 19 to 52 years. Participants were

recruited by informal advertising of the study. The study

was approved by the Human Ethics Committee of the S.

Marcos Hospital, in which data were collected. After

signing the written informed consent, all the participants

underwent a session of personality assessment followed by

an fMRI scanning session.

Personality assessment

All subjects completed the Portuguese adaption of the

NEO-Five Factor Inventory (NEO-FFI) (Costa and McCrae

1995). The NEO-FFI is the short version of the NEO-PIR.

It is a psychological personality inventory that measures

the five personality dimensions described by Costa and

McCrae: Extraversion, Agreeableness, Conscientiousness,

Neuroticism, and Openness to Experience. The instrument

has 60 items (12 per domain) answered on a five-point

Likert scale, ranging from ‘‘strongly disagree’’ to ‘‘strongly

agree.’’ NEO-FFI has good internal consistency for the

different subscales (N = 0.79, E = 0.79, O = 0.80,

A = 0.75, and C = 0.83). In addition, the test–retest reli-

ability and the external validity of the instrument are high.

The controversy about the 5-dimension solution for the

debate on the number of basic personality traits is in part

due to the fact that the Big Five are not totally independent

dimensions. In a recent meta-analysis, van der Linden et al.

(2010) collated the results of 212 Big Five studies that

reported intercorrelations among Big Five measures and

estimated the matrix of true intercorrelations (Cavanna and

Trimble 2006), concluding that the Big Five intercorrelate.

Specifically, Neuroticism tended to correlate negatively

with other dimensions (N–O: r = -0.17; N–C: r = -4,3,

N–E: r = -0.36, N–A: r = -0.36), while the other per-

sonality dimensions tended to correlate positively (ex:

E–O: r = 0.43, C–A: r = 0.43, C–E: r = 0.29, A–O:

r = 0.21 and C–O: r = 0.20). Using confirmatory factor

analyses they found also that the model in which the Big

Five was assumed to be uncorrelated (orthogonal) did not

fit the data. This intercorrelation nature of personality

measures was also considered in our study (N–E: r =

-0.28, p = 0.05; A–O: r = 0.49, p = 0.000, A–C:

r = 0.41, p = 0.003), when performing the correlations

with the DMN activity. For this study, all NEO-FFI

variables followed a normal distribution, assessed by

Kolmogorov–Smirnov and Shapiro tests [Neuroticism:

K–S(49) = 0.08, p = 0.20; Extraversion: K–S(49) = 0.07,

p = 0.20; Openness/Intellect: K–S(49) = 0.11, p = 0.18,

Agreeableness: K–S(49) = 0.10, p = 0.20, Conscien-

tiousness: S–W1(49) = 0.98, p = 0.05]. We did not

observe an association between sex and personality

dimensions (Chi square test, p [ 0.05) or between age and

personality scores (p [ 0.05). Our participants’ scores in

the different subscales, along with demographic data, are

summarized in Table 1.

fMRI acquisition

The brain’s gradient-echo echo-planar imaging (EPI)

BOLD fMRI acquisition sequence was conducted on a

clinically approved Siemens Magnetom Avanto 1.5 T in

the S. Marcos Hospital. During the task-free acquisition,

1 All variables but Conscientiousness followed a normal distribution

sample with p \ 0.05 in both tests (K–S and S-W) – here we included

only S-W test.

Brain Struct Funct (2014) 219:1913–1922 1915

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subjects were instructed to keep the eyes closed and to

think about nothing particular. This axial whole brain

sequence with 100 volumes was obtained with TR = 3 s,

TE = 50 ms, FA = 908, in-plane resolution =

3.4 9 3.4 mm2, 30 interleaved slices, thickness = 5 mm,

imaging matrix 64 9 64, and FoV = 220 mm.

Image processing

Prior to further processing and analysis of the different

image sequences, all the images were inspected to confirm

that they were not affected by critical head motion and

participants had no brain lesions. To achieve signal sta-

bilization and allow subjects to adjust to the scanner noise,

the first five volumes (15 s) were discarded. Data were

further pre-processed using SPM8 (Statistical Parametrical

Mapping, version 8, http://www.fil.ion.ucl.ac.uk). Func-

tional MRI data were corrected for errors in slice timing,

using first slice as reference and SPM80s Fourier phase shift

interpolation, to reduce different slice time acquisition.

Images were realigned to the mean image to correct head

motion with a six-parameter rigid-body spatial transfor-

mation and estimation was performed at 0.9 quality, 4-mm

separation, 5-mm FWHM smoothing kernel using second

degree B-Spline interpolation, and spatially normalized to

the Montreal Neurological Institute (MNI) standard coor-

dinate system using SPM8 EPI template and trilinear

interpolation. Data were then resampled every 3 mm using

sinc interpolation, smoothed to decrease spatial noise with

a 6 mm, full-width, half-maximum, Gaussian kernel and

temporally band-pass filtered (0.01–0.08 Hz) to reduce

physiological noise. All subjects displayed head motion

less than 2 mm in translation or 2� in rotation.

Independent component analysis

To study the functional networks involved in the task-free

BOLD sequence, spatial ICA analysis was performed using

the Group ICA 2.0d of fMRI Toolbox (GIFT, http://www.

icatb.sourceforge.net) (Calhoun et al. 2001a; Correa et al.

2005). The ICA analysis consists in extracting the indi-

vidual spatial independent maps and their related time

courses. The reduction of dimensionality of the functional

data and computational load was performed with principal

component analysis (PCA). The number of independent

components estimated was 20 for each subject, based a

good trade-off (clustering/splitting) between preserving the

information in the data while reducing its size (Beckmann

et al. 2005; Calhoun et al. 2001b). ICA calculation was

then performed using the iterative Infomax algorithm. The

ICASSO tool was used to control the ICA reliability.

Twenty computational runs were made on the dataset,

during which the components were being recomputed and

compared across runs and the robustness of the results was

ensured (Wei et al. 2012). The independent components

were obtained and each voxel of the spatial map was

expressed as a t statistic map, which was finally converted

to a z statistic that characterizes the degree of correlation of

the voxel signal with the component time course, providing

indirectly a degree of functional connectivity within the

network (Beckmann et al. 2005; Kunisato et al. 2011). The

components were sorted and spatially correlated with the

default mode template from GIFT for DMN identification

and were also visually inspected. Finally, the best-fit

component of each individual (z maps) was used to per-

form group statistical analyses (second level analyses).

Statistical analyses

For the group study, the SPM80s level General Linear

Model (GLM) method was used to analyze the pre-pro-

cessed datasets. All the individual default mode z maps

were included in the same group and a one-sample t test

(p \ 0.05 FWE corrected for multiple comparisons, extent

threshold k = 10 voxels) was used to study the global

pattern of DMN activation. Then a multiple regression

(with positive and negative correlations) was performed,

using all subjects in the study and each one of the Big Five

model personality dimensions, controlling for the other

four dimensions and for age and gender. Results were

considered significant at a corrected for multiple compar-

isons p \ 0.05 threshold (combined height threshold

p \ 0.01 and a minimum cluster size = 24, using

FWHM = 8 mm, rmm = 5 and 1,000 iterations), deter-

mined by Monte Carlo simulation program (AlphaSim).

The resulting statistical maps (t and z statistics) represent

the strength of the association between personality

dimensions and DMN functional activation. These were

presented using the same DMN template mask to sort the

components that were applied to the whole brain activation

patterns. Finally, T scores for the DMN areas were reported

and converted into a goodness of fit score (r2) and 95 % CI.

Table 1 Main demographic and personality measures

Main demographic and personality measures

Number of subjects 49

Men/women 19/30

Mean age, SD (range) M = 25.0, SD = 5.3

NEO-FFI scores

Neuroticism M = 22.5, SD = 4.71

Extraversion M = 30.5, SD = 4.94

Openness/Intellect M = 29.1, SD = 5.32

Agreeableness M = 28.49, SD = 7.09

Conscientiousness M = 32.28, SD = 5.44

1916 Brain Struct Funct (2014) 219:1913–1922

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Within the template, only the typical DMN regions were

reported and anatomical labeling was assigned by a com-

bination of visual inspection and Anatomical Automatic

Labeling atlas (AAL) (Krebs et al. 2009).

Results

DMN results

The DMN was identified in the resting state conditions at

the group level, and four main components were observed,

namely the PCCs and PCu, the MPFC, the bilateral inferior

parietal cortex (IPC), and the left inferior temporal cortex

(ITC) (Adelstein et al. 2011; see Fig. 1). The statistics of

the group DMN pattern are represented in Table 2. The

statistics of the group DMN pattern are represented in

Table 2.

Neuroticism and DMN components

Within the DMN, increased Neuroticism scores were

associated with decreased activity of the right superior

parietal cortex (x = 30, y = -72, z = 54, T = 3.02,

r2 = 0.18, IC 0.05–0.23) (see supplemental table and

Figs. 2, 3).

Extraversion and DMN components

Significant positive correlations were found between

Extraversion and the DMN, namely, the right precuneus

(x = 18, y = -51, z = 15; T = 3.74, r2 = 0.25, IC

0.04–0.11), bilateral superior parietal lobe (right: x = 15,

y = -66, z = 66, T = 3.72, r2 = 0.25, IC 0.05–0.18; left:

x = -39, y = -51, z = 63, T = 3.49, r2 = 0.23, IC

0.04–0.15) and left inferior parietal lobe (x = -42, y =

-60, z = 54, T = 3.21, r2 = 0.20, IC 0.05–0.21 (see

supplemental table and Figs. 2, 4).

Openness/Intellect and DMN component

Increased Openness/Intellect was associated with increased

activity in the right inferior parietal lobe (x = 48, y =

-57, z = 27, T = 5.02, r2 = 0.38, IC 0.12–0.28) and with

decreased activity in bilateral superior parietal cortex

(right: x = 42, y = -45, z = 66 T = 4.13, r2 = 0.29, IC

0.07–0.21; left: x = -24, y = -72, z = 54, T = 3.36

r2 = 0.22, IC 0.06–0.211) and in the left precuneus (x =

-15, y = -66, z = 57, T = 3.91, r2 = 0.27, IC

0.04–0.12) (see supplemental table and Figs. 2, 5).

Agreeableness and DMN components

The BOLD activity in specific components of the DMN

[the MPFC and ACC (x = 9, y = 42, z = 0, T = 4.31

r2 = 0.31, IC 0.03–0.08; x = -6, y = 39, z = -6,

T = 3.61, r2 = 0.24, IC 0.03–0.10] was positively corre-

lated with Agreeableness. Negative associations between

DMN and Agreeableness were evident in the right superior

parietal lobe (x = 30, y = -72, z = 60, T = 2.81,

r2 = 0.16, IC 0.03–0.16) (see supplemental table and

Figs. 2, 6).

Conscientiousness and DMN components

Finally, increased Conscientiousness scores were positively

associated with right superior parietal cortex (x = 33,

y = -78, z = 51, T = 3.36, r2 = 0.22, IC 0.04–0.16) and

negatively associated with brain activity in bilateral pre-

cuneus (x = -3, y = -78, z = 54, T = 4.75, r2 = 0.36,

IC 0.13–0.39; x = 18, y = -72, z = 45, T = 3.49,

r2 = 0.23, IC 0.04–0.13) and bilateral ACC (x = -3,

y = 48, z = 15, T = 3.70, r2 = 0.25, IC 0.04–0.12; x = 6,

y = 36, z = 9, T = 3.57, r2 = 0.24, IC 0.03–0.87) (see

supplemental table and Figs. 2, 7).

Fig. 1 Group activation pattern of DMN (p \ 0.05 FWE corrected)

Table 2 Group statistics—DMN activation (FWE \ 0.05 corrected,

extent threshold k = 10 voxels)

Regions Z k MNI coordinates (x, y, z)

Precuneus [8 2,134 0, -60, 33

Left parietal lobe [8 878 -42, -63, 33

Right parietal lobe [8 676 54, -66, 30

Medial prefrontal [8 1,835 3, 57, -3

Left medial temporal 6.37 18 -57, -42.0

Brain Struct Funct (2014) 219:1913–1922 1917

123

Discussion

To the best of our knowledge, this is one of the first studies

exploring the association between personality measures

and overall DMN functional activity. There is already an

evidence showing a relationship between the five factor

personality traits and RSFC between seed regions posi-

tioned within the anterior cingulate and the precuneus

(cognitive and affective ‘hubs’) and functional connectivity

with several brain areas within and outside the DMN

(Adelstein et al. 2011). In this study, we performed a more

restricted analysis, circumscribed to only typical DMN

regions.

Overall, we found evidence that the Extraversion (E),

and Agreeableness (A) dimensions were positively corre-

lated with the activity in the midline core of the DMN,

whereas Conscientiousness (C) and Openness (O) scores

were positively correlated with activation in the parietal

cortex systems. This is in line with our predictions,

namely that the midline core of the DMN would be more

associated with personality traits characterized by self-

referential processing (E and A), while the parietal lobe

system would be more related with non-self processing

traits (C and O).

In fact, Agreeableness was the only personality trait

positively associated with the MPFC and ACC, a DMN

component associated with social awareness, including the

ability to attribute mental state to others (Gusnard et al.

2001; Lane et al. 1998). Indeed, stronger activity in the

midline core of the DMN has been related with preferential

self-related activity (Andrews-Hanna et al. 2010) as emo-

tional state attribution, personal significance, motivation to

Fig. 2 a Positive correlations (p \ 0.05 corrected for multiple

comparisons) between Neuroticism (pink), Extraversion (green),

Openness/Intellect (yellow), Agreeableness (red), Conscientiousness

(blue) scores and DMN components; b negative correlations

(p \ 0.05 corrected for multiple comparisons) between Neuroticism

(pink), Extraversion (green), Openness/Intellect (yellow), Agreeable-

ness (red), Conscientiousness (blue) scores and DMN components

Fig. 3 Correlation plot for DMN components (functional connectivity) and Neuroticism scale

1918 Brain Struct Funct (2014) 219:1913–1922

123

Fig. 5 Correlation plot for DMN components (functional connectivity) and Openness scale

Fig. 6 Correlation plot for DMN components (functional connectivity) and Agreeableness scale

Fig. 4 Correlation plot for DMN components (functional connectivity) and Extraversion scale

Brain Struct Funct (2014) 219:1913–1922 1919

123

positive reinforcement, and social cognition. Together,

these dimensions reflect a pro-social orientation and the

ability to respond to the needs of others in an empathic

way, all of which are social-cognitive tasks (Extraversion

and Agreeableness-related dimensions) subserved by the

midline core of the DMN (Andrews-Hanna et al. 2010).

Additionally, and consistent with other studies, Extraver-

sion was also found to be positively associated with the

precuneus (Kunisato et al. 2011; Wei et al. 2012; Ryman

et al. 2011), a brain region playing an important role in

emotional regulation and self-related mental representa-

tions (Cavanna and Trimble 2006).

In accordance with this hypothesis, we observed that

these midline core areas (ACC and precuneus) were neg-

atively associated with a personality dimension character-

ized by less engagement in social-oriented tasks and self-

processing, but more self-regulation processes, such as

mental schemes elaboration, inhibition, self-discipline, and

planning (Conscientiousness). In comparison with the

midline core of the DMN, the parietal cortex system dis-

plays an important functional role in attentional control,

response inhibition (Garavan et al. 1999) and task switch-

ing (Sohn et al. 2000). Specifically, we observed a rela-

tionship between higher Conscientiousness and increased

bilateral parietal cortex activation, confirming our predic-

tion suggesting that parietal lobe system would be more

associated with non-self processing traits. In fact, these

functional properties of the parietal system have been

previously associated with the Conscientiousness and

Openness traits (DeYoung et al. 2010; Behrmann et al.

2004). In addition, higher Openness scores were associated

with higher activation in the inferior parietal lobe, although

this personality measure was negatively associated with

activity in the bilateral superior parietal cortex and left

precuneus. These results suggest that activity within the

parietal cortex is differentially associated with the ability to

process social, emotional, and sensorial stimuli. This

observation is in accordance with evidence showing that

parietal regions have an additional role in processing more

outward-directed social or contextual stimuli (Johnson et al.

2006; Volkow et al. 2011; Johnson et al. 1999) and with

data derived from our study, namely the differential relation

between these brain regions and Extraversion and Neurot-

icism. Therefore, while Extraversion was associated with

greater regional cerebral flow in the parietal cortex system

(superior and inferior parietal cortex), stronger activity in

the right superior parietal cortex was associated with lower

Agreeableness and Neuroticism scores. Right superior

parietal cortex has been associated with response to nega-

tive pictures and meaning (e.g., categorization of sentences)

(Chan et al. 2008), suggesting that personality (E, A, and N)

modulates differently the effects of emotional arousal and

valence on brain activation (Kehoe et al. 2011), as already

noted. Indeed, these results are consistent with indirect

evidence showing an abnormal activity of this brain region

in individuals with autism spectrum disorders when are

engaged in social-oriented tasks (Greene et al. 2011).

As previously mentioned, the main objective of this

study was to ascertain whether the reports on the associ-

ation between structural neuroanatomy and the Big Five

personality trait taxonomy would be confirmed at the

functional level. As DeYoung et al. (2010) argued,

although it is reasonable to think ‘‘volume tends to covary

positively with function,’’ (p.822) we must be cautious

when making predictions about the direction of the effect.

The present study not only confirms previous data on the

functional correlates of the Big Five personality dimen-

sions, but also expands our understanding of this rela-

tionship by showing that different personality dimensions

are associated with specific patterns of activation in a

brain default mode. Moreover, we did not replicate the

association between Big Five traits and specific brain

Fig. 7 Correlation plot for DMN components (functional connectivity) and Conscientiousness scale

1920 Brain Struct Funct (2014) 219:1913–1922

123

regions reported by previous studies (Adelstein et al.

2011; Sutin et al. 2009; DeYoung et al. 2010). These may

be due to the different methodological approaches; spe-

cifically, the use of different resting state networks and

different analysis procedures—for example, Adelstein

et al. (2011) used a ROI seed based regions analysis,

whereas we used an independent component analysis

approach. Indeed, as the authors acknowledge, the com-

plexity of brain–behavior interaction is such that it

requires us to look for personality networks that may link

different brain regions. Our findings constitute an impor-

tant step in this direction, namely by identifying associa-

tions between specific personality traits and key DMN

regions. Moreover, other studies have been linking per-

sonality traits with other RSNs that were not tackled in

this study and should be addressed in future studies. Once

data on resting activity underlying personality are more

consistent, we will be able to derive important clinical

implications, namely to predict which alterations in the

resting state activity are likely to increase the vulnerability

for several Axis I psychiatric disorders, known to be

associated with specific personality traits.

Although the promising evidence found in our study, our

results should be interpreted with caution due to several

methodological limitations. First it is important to highlight

that we used a very specific index of DMN activity (ICA

z scores) and our results should be interpreted taking into

account that methodological option. Another limitation is

the correlational nature of our data, preventing the estab-

lishment of a causal relationship between personality

dimensions and DMN patterns. We believe this relationship

is a complex one because the psychological tasks supported

by the DMN tend to activate multiple regions within the

network. Moreover, as Buckner et al. (2008) suggested,

these tasks share core processes in common but differ in

terms of the content and goal to which these processes are

applied, which in turn may determine the transient inter-

actions between the DMN components and other brain

systems. Different personality dimensions influence the

individual’s tendency to engage in different cognitive or

emotional functions, but the specific nature of their internal

mental activity may also play a role. Indeed, this is difficult

to overcome in this study since we are using resting state

scans with less control over what cognitive/emotional pro-

cesses are being engaged during the scan. Therefore, future

studies should try to conciliate the assessment of the sub-

jects’ personality dimensions with the specific content of

their spontaneous cognitive activity at rest.

Acknowledgments This research was funded by PIC/IC/83290/

2007, which is supported by FEDER (POFC—COMPETE) and FCT.

The authors acknowledge Jaime Rocha for his discussions on

neuroimaging.

References

Adelstein JS, Shehzad Z, Mennes M, Deyoung CG, Zuo XN, Kelly C,

Margulies DS, Bloomfield A, Gray JR, Castellanos FX, Milham

MP (2011) Personality is reflected in the brain’s intrinsic

functional architecture. PLoS One 6(11):e27633. doi:10.1371/

journal.pone.0027633

Andrews-Hanna JR, Reidler JS, Sepulcre J, Poulin R, Buckner RL

(2010) Functional-anatomic fractionation of the brain’s default

network. Neuron 65(4):550–562. doi:10.1016/j.neuron.2010.02.

005

Beckmann CF, DeLuca M, Devlin JT, Smith SM (2005) Investiga-

tions into resting-state connectivity using independent compo-

nent analysis. Philos Trans R Soc Lond B Biol Sci

360(1457):1001–1013. doi:10.1098/rstb.2005.1634

Behrmann M, Geng JJ, Shomstein S (2004) Parietal cortex and

attention. Curr Opin Neurobiol 14(2):212–217. doi:10.1016/j.

conb.2004.03.012

Buckner RL, Andrews-Hanna JR, Schacter DL (2008) The brain’s

default network: anatomy, function, and relevance to disease.

Ann N Y Acad Sci 1124:1–38

Calhoun VD, Adali T, Pearlson GD, Pekar JJ (2001a) A method for

making group inferences from functional MRI data using

independent component analysis. Hum Brain Mapp 14:140–151

Calhoun VD, Adali T, Pearlson GD, Pekar JJ (2001b) Spatial and

temporal independent component analysis of functional MRI

data containing a pair of task-related waveforms. Hum Brain

Mapp 13:43–53

Cavanna AE, Trimble MR (2006) The precuneus: a review of its

functional anatomy and behavioural correlates. Brain: J Neurol

129(Pt 3):564–583. doi:10.1093/brain/awl004

Chan SW, Harmer CJ, Goodwin GM, Norbury R (2008) Risk for

depression is associated with neural biases in emotional

categorisation. Neuropsychologia 46(12):2896–2903. doi:10.

1016/j.neuropsychologia.2008.05.030

Cherkassky VL, Kana RK, Keller TA, Just MA (2006) Functional

connectivity in baseline resting-state network in autism. Neuro-

report 17:1687–1690

Correa N, Adali T, Li Y, Calhoun V (2005) Comparison of blind

source separation algorithms for FMRI using a new Matlab

toolbox: GIFT. Proc IEEE Int Conf Acoust Speech Signal

Process 5:401–404

Costa PT, McCrae RR (1992) Revised NEO personality inventory

(NEOPI- R) and the NEO five-factor inventory (NEO-FFI)

professional manual. Psychological Assessment Resources,

Odessa, FL

Costa PT, McCrae RR (1995) Domains and facets: hierarchical

personality assessment using the revised NEO personality

inventory. J Pers Assess 64:21–50

Deckersbach T, Miller KK, Klibanski A, Fischman A, Dougherty DD,

Blais MA, Herzog DB, Rauch SL (2006) Regional cerebral brain

metabolism correlates of neuroticism and extraversion. Depress

Anxiety 23(3):133–138. doi:10.1002/da.20152

DeYoung CG (2010) Personality neuroscience and the biology of

traits. Soc Pers Psychol Compass 4(12):1165–1180

DeYoung CG, Hirsh JB, Shane MS, Papademetris X, Rajeevan N,

Gray JR (2010) Testing predictions from personality neurosci-

ence. Psychol Sci 21(6):820

Fleeson W, Noftle EE (2009) In favor of the synthetic resolution to

the person-situation debate. J Res Pers 43(2):150–154

Garavan H, Ross TJ, Stein EA (1999) Right hemispheric dominance

of inhibitory control: an event-related functional MRI study.

Proc Natl Acad Sci USA 96(14):8301–8306

Greene DJ, Colich N, Iacoboni M, Zaidel E, Bookheimer SY,

Dapretto M (2011) Atypical neural networks for social orienting

Brain Struct Funct (2014) 219:1913–1922 1921

123

in autism spectrum disorders. Neuroimage 56(1):354–362.

doi:10.1016/j.neuroimage.2011.02.031

Greicius MD, Krasnow B, Reiss AL, Menon V (2003) Functional

connectivity in the resting brain: a network analysis of the

default mode hypothesis. Proc Natl Acad Sci USA 100(1):

253–258. doi:10.1073/pnas.0135058100

Gusnard DA, Akbudak E, Shulman GL, Raichle ME (2001) Medial

prefrontal cortex and self-referential mental activity: relation to a

default mode of brain function. In: Proceedings of the National

Academy of Sciences of the United States of America

Johnson DL, Wiebe JS, Gold SM, Andreasen NC, Hichwa RD,

Watkins GL, Boles Ponto LL (1999) Cerebral blood flow and

personality: a positron emission tomography study. Am J

Psychiatry 156(2):252–257

Johnson MK, Raye CL, Mitchell KJ, Touryan SR, Greene EJ, Nolen-

Hoeksema S (2006) Dissociating medial frontal and posterior

cingulate activity during self-reflection. Soc Cogn Affect

Neurosci 1(1):56–64. doi:10.1093/scan/nsl004

Kehoe EG, Toomey JM, Balsters JH, Bokde AL (2011) Personality

modulates the effects of emotional arousal and valence on brain

activation. Soc Cogn Affect Neurosci. doi:10.1093/scan/nsr059

Kennedy DP, Courchesne E (2008) The intrinsic functional organi-

sation of the brain is altered in autism. Neuroimage 39:1877–

1885

Kim SH, Hwang JH, Park HS, Kim SE (2008) Resting brain

metabolic correlates of neuroticism and extraversion in young

men. Neuroreport 19(8):883–886. doi:10.1097/WNR.0b013e

328300080f

Krebs RM, Schott BH, Duzel E (2009) Personality traits are

differentially associated with patterns of reward and novelty

processing in the human substantia nigra/ventral tegmental area.

Biol Psychiatry 65(2):103–110. doi:10.1016/j.biopsych.2008.08.

019

Kunisato Y, Okamoto Y, Okada G, Aoyama S, Nishiyama Y, Onoda

K, Yamawaki S (2011) Personality traits and the amplitude of

spontaneous low-frequency oscillations during resting state.

Neurosci Lett 492(2):109–113. doi:10.1016/j.neulet.2011.01.067

Lane RD, Reiman EM, Axelrod B, Yun LS, Holmes A, Schwartz GE

(1998) Neural correlates of levels of emotional awareness.

Evidence of an interaction between emotion and attention in the

anterior cingulate cortex. J Cogn Neurosci 10(4):525–535

Mason MF, Norton MI, Van Horn JD, Wegner DM, Grafton ST,

Macrae CN (2007) Wandering minds: the default network and

stimulus independent thought. Science 315:393–395

Mischel W (1968) Personality and assessment. John Wiley, New

York

Raichle ME, Snyder AZ (2007) A default mode of brain function: a

brief history of an evolving idea. Neuroimage 37(4):1083–1090.

doi:10.1016/j.neuroimage.2007.02.041 (discussion 1097–1089)

Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA,

Shulman GL (2001) A default mode of brain function. Proc Natl

Acad Sci 98:676–682

Ryman SG, Gasparovic C, Bedrick EJ, Flores RA, Marshall AN, Jung

RE (2011) Brain biochemistry and personality: a magnetic

resonance spectroscopy study. PLoS One 6(11):e26758. doi:10.

1371/journal.pone.0026758

Sohn MH, Ursu S, Anderson JR, Stenger VA, Carter CS (2000) The

role of prefrontal cortex and posterior parietal cortex in task

switching. Proc Natl Acad Sci USA 97(24):13448–13453.

doi:10.1073/pnas.240460497

Sutin AR, Beason-Held LL, Resnick SM, Costa PT (2009) Sex

differences in resting-state neural correlates of openness to

experience among older adults. Cereb Cortex 19(12):2797–2802.

doi:bhp06610.1093/cercor/bhp066

van der Linden D, Nijenhuis J, Bakker AB (2010) The general factor

of personality: a meta-analysis of big five intercorrelations and a

criterion-related validity study. J Res Personal 44(3):315–327

Volkow ND, Tomasi D, Wang GJ, Fowler JS, Telang F, Goldstein

RZ, Alia-Klein N, Woicik P, Wong C, Logan J, Millard J,

Alexoff D (2011) Positive emotionality is associated with

baseline metabolism in orbitofrontal cortex and in regions of

the default network. Mol Psychiatry. doi:10.1038/mp.2011.30

Wei L, Duan X, Yang Y, Liao W, Gao Q, Ding JR, Zhang Z, Zeng W,

Li Y, Lu G, Chen H (2011) The synchronization of spontaneous

BOLD activity predicts extraversion and neuroticism. Brain Res

1419:68–75. doi:10.1016/j.brainres.2011.08.060

Wei L, Duan X, Zheng C, Wang S, Gao Q, Zhang Z, Lu G, Chen H

(2012) Specific frequency bands of amplitude low-frequency

oscillation encodes personality. Hum Brain Mapp. doi:10.1002/

hbm.22176

Wright CI, Williams D, Feczko E, Barrett LF, Dickerson BC,

Schwartz CE, Wedig MM (2006) Neuroanatomical correlates of

extraversion and neuroticism. Cereb Cortex 16(12):1809–1819.

doi:bhj11810.1093/cercor/bhj118

Zhou Y, Liang M, Tian L, Wang K, Hao Y, Liu H, Liu Z, Jiang T

(2007) Functional disintegration in paranoid schizophrenia using

resting-state fMRI. Schizophr Res 97:194–205

1922 Brain Struct Funct (2014) 219:1913–1922

123